About Lily AI:

Lily AI is a female-founded retail AI company empowering retailers and brands by bridging the gap between merchant-speak and customer-speak. Leveraging computer vision, natural language processing, machine learning, and vertical-specific large language models (LLMs), Lily AI enhances customer shopping experiences by injecting consumer-centric language throughout the retail technology ecosystem. Interoperable with leading eCommerce platforms, Lily AI maximizes existing tech investments to deliver upwards of 9-figure revenue lift through improved product attribution, enhanced discovery, and higher customer conversion. Learn more at www.lily.ai.

Overview:

Our vision is to create the richest, most comprehensive product knowledge graph that connects suppliers, buyers and consumers across ecommerce. As part of the Machine Learning team, you will be at the forefront of developing innovations to enhance product discoverability online. Joining a small, high-impact team, you will build machine learning pipelines to extract, reconcile and expand fine-grained attributes and metadata from multi-modal product data at scale. Day-to-day, you will build and prototype feature enrichment models leveraging state-of-the-art NLP and CV. Collaborating closely with the rest of the team, you will productionize these models into robust pipelines, continuously processing millions of products daily. You will also get opportunities to showcase your work to key technical and non-technical stakeholders.

Key Responsibilities:

Model Development: Build, evaluate, and optimize deep learning models for computer vision and NLP tasks such as image classification, image similarity, semantic understanding, etc. Study and implement state-of-the-art research techniques on team projects

Config Management: Maintaining taxonomy and configuration schemas for the team. This includes adding new categories, updating schema changes, and mapping raw values to taxonomy terms.

Data Analysis: Perform exploratory data analysis under guidance to uncover insights and patterns to facilitate model development and solution design.

Experimentation: Design and execute ML experiments, evaluate model performance against metrics like accuracy, recall, precision etc.

Deployment: Contribute to deployment of machine learning models to production by integrating with application codebases and pipelines with a focus on maintainability and reproducibility.

Iteration: Help design systems to continuously collect new data, retrain models and upgrade systems to enhance value delivery over time.

Collaboration: Work closely with domain experts, product team and senior ML scientists to understand business needs and implement effective software solutions. This would involve developing tools and prototypes based on guidance.

Documentation: Document code, APIs, and processes to facilitate collaboration and knowledge sharing within the team. Adopt best practices for version control and repository usage.

What we consider critical for this role:

• Bachelors/Masters in computer science, electrical engineering or a related field
• Strong understanding of ML fundamentals
• Coursework or ideally 1-2 years hands-on experience with Python, TensorFlow, PyTorch, etc
• Completed ML-focused academic projects demonstrating coding abilities
• Strong drive to quickly pick up new technical skills
• Able to discuss complex ML concepts clearly and collaborate cross-functionally for solutions

Currently, we are hiring from the following states – (candidates must be in current residence or open to relocating):

Alabama • Arizona • California • Florida • Georgia • Illinois • Indiana Massachusetts • Minnesota • Nevada • New Jersey • New York • North Carolina • Rhode Island • Tennessee • Texas • Utah • Virginia • Washington

Compensation is competitive and will be determined based on a combination of experience, seniority, internal, external equity and location. For some context: this position in the US would pay between $130,500-$192,250 USD per year, depending on experience and seniority. In other regions, compensation will be adjusted for local currency and local market rates. Lily AI compensation policy is calculated with a focus on equity and where employees can thrive.